6 results on '"Butakoff, Constantine"'
Search Results
2. Atrial functional and geometrical remodeling in highly trained male athletes: for better or worse?
- Author
-
Gabrielli, Luigi, Bijnens, Bart H., Butakoff, Constantine, Duchateau, Nicolas, Montserrat, Silvia, Merino, Beatriz, Gutierrez, Josep, Paré, Carles, Mont, Lluis, Brugada, Josep, and Sitges, Marta
- Published
- 2014
- Full Text
- View/download PDF
3. Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy
- Author
-
Cikes, Maja, Sanchez-Martinez, Sergio, Claggett, Brian, Duchateau, Nicolas, Piella, Gemma, Butakoff, Constantine, Pouleur, Anne Catherine, Knappe, Dorit, Biering-Sørensen, Tor, Kutyifa, Valentina, Moss, Arthur, Stein, Kenneth, Solomon, Scott, Bijnens, Bart, University of Zagreb, Universitat Pompeu Fabra [Barcelona] (UPF), Brigham & Women’s Hospital [Boston] (BWH), Harvard Medical School [Boston] (HMS), Imagerie et modélisation Vasculaires, Thoraciques et Cérébrales (MOTIVATE), Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modeling & analysis for medical imaging and Diagnosis (MYRIAD), Department of Cardiovascular Diseases [Woluwe-Saint-Lambert, Belgium] (Division of Cardiology ), Université Catholique de Louvain = Catholic University of Louvain (UCL)-Cliniques universitaires St Luc [Bruxelles]-Institut de Recherche Expérimentale et Clinique - IREC [Woluwe-Saint-Lambert, Belgium], University Heart Center Hamburg, Herlev and Gentofte Hospital, University of Rochester [USA], Boston Scientific, and Institució Catalana de Recerca i Estudis Avançats (ICREA)
- Subjects
Cardiac resynchronization therapy ,Science & Technology ,Cardiac & Cardiovascular Systems ,DEVICES ,Heart failure ,Personalized medicine ,MYOCARDIAL MOTION ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Echocardiography ,Machine learning ,Cardiovascular System & Cardiology ,Cardiac resynchronisation therapy ,CRT ,Personalised medicine ,[SDV.IB]Life Sciences [q-bio]/Bioengineering ,PREDICTORS ,Life Sciences & Biomedicine ,DEFIBRILLATOR IMPLANTATION TRIAL - Abstract
AIMS: We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). METHODS AND RESULTS: We studied 1106 HF patients from the Multicenter Automatic Defibrillator Implantation Trial with Cardiac Resynchronization Therapy (MADIT-CRT) (left ventricular ejection fraction ≤ 30%, QRS ≥ 130 ms, New York Heart Association class ≤ II) randomized to CRT with a defibrillator (CRT-D, n = 677) or an implantable cardioverter defibrillator (ICD, n = 429). An unsupervised ML algorithm (Multiple Kernel Learning and K-means clustering) was used to categorize subjects by similarities in clinical parameters, and left ventricular volume and deformation traces at baseline into mutually exclusive groups. The treatment effect of CRT-D on the primary outcome (all-cause death or HF event) and on volume response was compared among these groups. Our analysis identified four phenogroups, significantly different in the majority of baseline clinical characteristics, biomarker values, measures of left and right ventricular structure and function and the primary outcome occurrence. Two phenogroups included a higher proportion of known clinical characteristics predictive of CRT response, and were associated with a substantially better treatment effect of CRT-D on the primary outcome [hazard ratio (HR) 0.35; 95% confidence interval (CI) 0.19-0.64; P = 0.0005 and HR 0.36; 95% CI 0.19-0.68; P = 0.001] than observed in the other groups (interaction P = 0.02). CONCLUSIONS: Our results serve as a proof-of-concept that, by integrating clinical parameters and full heart cycle imaging data, unsupervised ML can provide a clinically meaningful classification of a phenotypically heterogeneous HF cohort and might aid in optimizing the rate of responders to specific therapies. ispartof: EUROPEAN JOURNAL OF HEART FAILURE vol:21 issue:1 pages:74-85 ispartof: location:England status: published
- Published
- 2019
4. Persistence of Cardiac Remodeling in Preadolescents With Fetal Growth Restriction.
- Author
-
Sarvari, Sebastian Imre, Rodriguez-Lopez, Merida, Nuñez-Garcia, Marta, Sitges, Marta, Sepulveda-Martinez, Alvaro, Camara, Oscar, Butakoff, Constantine, Gratacos, Eduard, Bijnens, Bart, and Crispi, Fatima
- Abstract
Background--Fetal growth restriction (FGR) affects 5% to 10% of newborns and is associated with increased cardiovascular mortality in adulthood. We evaluated whether prenatal cardiovascular changes previously demonstrated in FGR persist into preadolescence. Methods and Results--A cohort study of 58 FGR (defined as birth weight below 10th centile) and 94 normally grown fetuses identified in utero and followed-up into preadolescence (8-12 years of age) by echocardiography and 3-dimensional shape computational analysis. Compared with controls, FGR preadolescents had a different cardiac shape, with more spherical and smaller hearts. Left ventricular ejection fraction was similar among groups, whereas FGR had decreased longitudinal motion (decreased mitral annular systolic peak velocities: control median, 0.11 m/s [interquartile range, 0.09-0.12] versus FGR median 0.09 m/s [interquartile range, 0.09-0.10]; P<0.01) and impaired relaxation (isovolumic relaxation time: control, 0.21 ms [interquartile range, 0.12-0.35] versus FGR, 0.35 ms [interquartile range, 0.20-0.46]; P=0.04). Global longitudinal strain was decreased (control mean, -22.4% [SD, 1.37] versus FGR mean, -21.5% [SD, 1.16]; P<0.001) compensated by an increased circumferential strain and with a higher prevalence of postsystolic shortening in FGR as compared with controls. These differences persisted after adjustment for parental ethnicity and smoking, prenatal glucocorticoid administration, preeclampsia, gestational age at delivery, days in intensive care unit, sex, age, and body surface area at evaluation. Conclusions--This study provides evidence that cardiac remodeling induced by FGR persists until preadolescence with findings similar to those reported in their prenatal life and childhood. The findings support the hypothesis of primary cardiac programming in FGR for explaining the association between low birth weight and cardiovascular risk in adulthood. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
5. Machine-learning based phenogrouping in heart failure to identify responders to resynchronization therapy
- Author
-
Cikes, Maja, Sanchez Martinez, Sergio, Claggett, Brian, Duchateau, Nicolas, Piella Fenoy, Gemma, Butakoff, Constantine, Pouleur, Anne Catherine, Knappe, Dorit, Biering‐Sørensen, Tor, Kutyifa, Valentina, Moss, Arthur, Stein, Kenneth, Solomon, Scott D., and Bijnens, Bart
- Subjects
Cardiac resynchronization therapy ,Echocardiography ,Machine learning ,Heart failure ,Personalized medicine - Abstract
We tested the hypothesis that a machine learning (ML) algorithm utilizing both complex echocardiographic data and clinical parameters could be used to phenogroup a heart failure (HF) cohort and identify patients with beneficial response to cardiac resynchronization therapy (CRT). The work of S. Sanchez‐Martinez was supported by a fellowship from ‘la Caixa’ Banking Foundation. C. Butakoff was supported by a grant from the Fundació La Marató de TV3 (n. 20154031), Spain. N. Duchateau was supported by ‘Programme Avenir Lyon Saint‐Etienne’ (PALSE‐IMPULSION‐2016, Lyon, France). MADIT‐CRT was sponsored by Boston Scientific, while no additional funding was provided for this analysis. This study was also partially supported by the Spanish Ministry of Economy and Competitiveness (grant TIN2014‐52923‐R; Maria de Maeztu Units of Excellence Programme ‐ MDM‐2015‐0502) and FEDER.
6. Multiview diffeomorphic registration: Application to motion and strain estimation from 3D echocardiography
- Author
-
Piella, Gemma, Craene, Mathieu De, Butakoff, Constantine, Grau, Vicente, Yao, Cheng, Nedjati-Gilani, Shahrum, Penney, Graeme P., and Frangi, Alejandro F.
- Subjects
- *
DIFFEOMORPHISMS , *ECHOCARDIOGRAPHY , *THREE-dimensional imaging in biology , *MYOCARDIUM , *HEART beat , *ULTRASONIC imaging , *DIAGNOSIS , *WOUNDS & injuries - Abstract
Abstract: This paper presents a new registration framework for quantifying myocardial motion and strain from the combination of multiple 3D ultrasound (US) sequences. The originality of our approach lies in the estimation of the transformation directly from the input multiple views rather than from a single view or a reconstructed compounded sequence. This allows us to exploit all spatiotemporal information available in the input views avoiding occlusions and image fusion errors that could lead to some inconsistencies in the motion quantification result. We propose a multiview diffeomorphic registration strategy that enforces smoothness and consistency in the spatiotemporal domain by modeling the 4D velocity field continuously in space and time. This 4D continuous representation considers 3D US sequences as a whole, therefore allowing to robustly cope with variations in heart rate resulting in different number of images acquired per cardiac cycle for different views. This contributes to the robustness gained by solving for a single transformation from all input sequences. The similarity metric takes into account the physics of US images and uses a weighting scheme to balance the contribution of the different views. It includes a comparison both between consecutive images and between a reference and each of the following images. The strain tensor is computed locally using the spatial derivatives of the reconstructed displacement fields. Registration and strain accuracy were evaluated on synthetic 3D US sequences with known ground truth. Experiments were also conducted on multiview 3D datasets of 8 volunteers and 1 patient treated by cardiac resynchronization therapy. Strain curves obtained from our multiview approach were compared to the single-view case, as well as with other multiview approaches. For healthy cases, the inclusion of several views improved the consistency of the strain curves and reduced the number of segments where a non-physiological strain pattern was observed. For the patient, the improvement (pacing ON vs. OFF) in synchrony of regional strain correlated with clinician blind assessment and could be seen more clearly when using the multiview approach. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
- View/download PDF
Catalog
Discovery Service for Jio Institute Digital Library
For full access to our library's resources, please sign in.